Abstract: Design and Analysis of an Algorithm for Classification of Spatio-temporal Data and its Application in Autism Diagnosis In this thesis an algorithm is proposed for the detection and analysis of patterns in Spatio-Temporal data. The results of this research are applicable to medical fields such as autism screening. Vast data gathering is a tiresome and difficult task, especially in medical experiments, therefore, one of the focuses of this research was on the design of a classifier with reduced number of features. Additionally, to prohibit over parametrization in the classifier, it is essential to reduce the size of the feature vector by an appropriate method. several feature selection methods were proposed and implemented, identifying each one’s advantages and disadvantages with validation and evaluation methods. Finally, using the best method for feature vector processing we were able to design classifiers with improved correct classification rate. A total number of 44 features were designated for the recognition of repetitive and periodic patterns. In order to test the designed algorithms and methods a database of raw experimental data was required. An intelligent toy car was introduced which can collect and save movement data while the subjects play with it. An instantaneous accelerometer embedded in a Nintendo Wii Remote was used to the sense and transfer accelerations in three orthogonal spatial axes to a computer. After preconditioning and processing the acquired signals, features were extracted from them which in turn were used for classification of autistic children from normal ones. The final results showed a correct classification rate of 87 percent using a forward selection method and SVM classifier with kernel of degree 5.

Abstract: Cellular Automata (Automaton) is one of the new scientific means to predict and simulate some physical and biological phenomena such as : Forest fire propagation, gen-evolution in biological systems , pedestrian movement process. A Cellular Automaton (pl. cellular automata, abbrev. CA) is a discrete model studied in computability theory, mathematics, physics, complexity science, theoretical biology and microstructure modeling. Cellular automata are also called cellular spaces, tessellation automata, homogeneous structures, cellular structures, tessellation structures, and iterative arrays . A cellular automaton consists of a regular grid of cells, each in one of a finite number of states, such as on and off (in contrast to a coupled map lattice). The grid can be in any finite number of dimensions. For each cell, a set of cells called its neighborhood (usually including the cell itself) is defined relative to the specified cell. An initial state (time t=0) is selected by assigning a state for each cell. A new generation is created, according to some fixed rule (generally, a mathematical function) that determines the new state of each cell in terms of the current state of the cell and the states of the cells in its neighborhood. Typically, the rule for updating the state of cells is the same for each cell and does not change over time, and is applied to the whole grid simultaneously, though exceptions are known, such as the probabilistic cellular automata and asynchronous cellular automaton. The inspiration for this approach comes from complex natural systems such as insect colonies, nervous systems, and economic systems. The goal is to understand how computation occurs in an evolving decentralized system.